"Pilot AI Vision Framework: From Doorbells to Defense," a Presentation from Pilot AI

Pilot AI’s Vision Framework has enabled real-time detection, classification and tracking in thousands of devices, from consumer applications to federal contracts. Though diverse in end-user application, these use cases all share a common disadvantage: they are compute-constrained. Small consumer electronics are compute-constrained as BoM cost limits the amount of silicon that can be integrated, whereas federal use cases are compute-constrained since the problem (seeing from thousands of feet in the sky) requires processing a tremendous amount of data in real-time without reliable network connectivity.

Scaling a single framework to enable the diverse set of hardware platforms these applications represent, from ultra-low power DSPs and microcontrollers to full-size GPUs, is what differentiates Pilot AI’s Vision Framework. Su introduces Pilot AI’s deep learning-based computer vision framework for compute-constrained devices and demonstrates this framework in real-world applications to motivate the drive towards embedded deep learning.